390 research outputs found
Consensus Message Passing for Layered Graphical Models
Generative models provide a powerful framework for probabilistic reasoning.
However, in many domains their use has been hampered by the practical
difficulties of inference. This is particularly the case in computer vision,
where models of the imaging process tend to be large, loopy and layered. For
this reason bottom-up conditional models have traditionally dominated in such
domains. We find that widely-used, general-purpose message passing inference
algorithms such as Expectation Propagation (EP) and Variational Message Passing
(VMP) fail on the simplest of vision models. With these models in mind, we
introduce a modification to message passing that learns to exploit their
layered structure by passing 'consensus' messages that guide inference towards
good solutions. Experiments on a variety of problems show that the proposed
technique leads to significantly more accurate inference results, not only when
compared to standard EP and VMP, but also when compared to competitive
bottom-up conditional models.Comment: Appearing in Proceedings of the 18th International Conference on
Artificial Intelligence and Statistics (AISTATS) 201
Randomized Optimum Models for Structured Prediction
One approach to modeling structured discrete data is to describe the probability of states via an energy function and Gibbs distribution. A recurring difficulty in these models is the computation of the partition function, which may require an intractable sum. However, in many such models, the mode can be found efficiently even when the partition function is unavailable. Recent work on Perturb-and-MAP (PM) models (Papandreou and Yuille, 2011) has exploited this discrepancy to approximate the Gibbs distribution for Markov random fields (MRFs). Here, we explore a broader class of models, called Randomized Optimum models (RandOMs), which include PM as a special case. This new class of models encompasses not only MRFs, but also other models that have intractable partition functions yet permit efficient mode-finding, such as those based on bipartite matchings, shortest paths, or connected components in a graph. We develop likelihood-based learning algorithms for RandOMs, which, empirical results indicate, can produce better models than PM.Engineering and Applied Science
The Effect of Bacterial Endotoxin Upon the Morphology of the Tectorial Membrane and Stereocilia in the Guinea Pig Cochlea
Endotoxin of E coli was microperfused into scala tympani or injected into the cerebrospinal fluid in anaesthetised pigmented guinea pigs. The effects of endotoxin on the cochlea were studied using electrophysiological techniques and scanning electron microscopy. We found a drop in the amplitude of the cochlear microphonics and compound action potentials 2 to 2.5 hours after injection. There were also changes in the morphology of stereocilia and the tectorial membrane. The stereocilia lost their rigidity and the tectorial membrane appeared swollen. These effects were less severe in animals which were pretreated with dexamethasone
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Fast Exact Inference for Recursive Cardinality Models
Cardinality potentials are a generally useful class of high order potential that affect probabilities based on how many of D binary variables are active. Maximum a posteriori (MAP) inference for cardinality potential models is well-understood, with efficient computations taking O(D log D) time. Yet efficient marginalization and sampling have not been addressed as thoroughly in the machine learning community. We show that there exists a simple algorithm for computing marginal probabilities and drawing exact joint samples that runs in O(D log2 D) time, and we show how to frame the algorithm as efficient belief propagation in a low order tree-structured model that includes additional auxiliary variables. We then develop a new, more general class of models, termed Recursive Cardinality models, which take advantage of this efficiency. Finally, we show how to do efficient exact inference in models composed of a tree structure and a cardinality potential. We explore the expressive power of Recursive Cardinality models and empirically demonstrate their utility.Engineering and Applied Science
Learning to Fix Build Errors with Graph2Diff Neural Networks
Professional software developers spend a significant amount of
time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning
architecture, called Graph2Diff, for automatically localizing and
fixing build errors. We represent source code, build configuration
files, and compiler diagnostic messages as a graph, and then use a
Graph Neural Network model to predict a diff. A diff specifies how
to modify the code’s abstract syntax tree, represented in the neural
network as a sequence of tokens and of pointers to code locations.
Our network is an instance of a more general abstraction which we
call Graph2Tocopo, which is potentially useful in any development
tool for predicting source code changes. We evaluate the model on
a dataset of over 500k real build errors and their resolutions from
professional developers. Compared to the approach of DeepDelta
[23], our approach tackles the harder task of predicting a more
precise diff but still achieves over double the accuracy
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Learning to Fix Build Errors with Graph2Diff Neural Networks
Professional software developers spend a significant amount of
time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning
architecture, called Graph2Diff, for automatically localizing and
fixing build errors. We represent source code, build configuration
files, and compiler diagnostic messages as a graph, and then use a
Graph Neural Network model to predict a diff. A diff specifies how
to modify the code’s abstract syntax tree, represented in the neural
network as a sequence of tokens and of pointers to code locations.
Our network is an instance of a more general abstraction which we
call Graph2Tocopo, which is potentially useful in any development
tool for predicting source code changes. We evaluate the model on
a dataset of over 500k real build errors and their resolutions from
professional developers. Compared to the approach of DeepDelta
[23], our approach tackles the harder task of predicting a more
precise diff but still achieves over double the accuracy
Popular attitudes to memory, the body, and social identity : the rise of external commemoration in Britain, Ireland, and New England
A comparative analysis of samples of external memorials from burial grounds in Britain, Ireland and New England reveals a widespread pattern of change in monument style and content, and exponential growth in the number of permanent memorials from the 18th century onwards. Although manifested in regionally distinctive styles on which most academic attention has so far been directed, the expansion reflects global changes in social relationships and concepts of memory and the body. An archaeological perspective reveals the importance of external memorials in articulating these changing attitudes in a world of increasing material consumption
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